AI/ML Principal Data Scientist

Client is seeking a Principal Machine Learning Engineer to provide technical strategy and architecture leadership across Machine Learning, AI, and Generative AI initiatives within the legal domain. This is a deeply technical individual-contributor leadership role for an engineer who began their career in software development and later transitioned into ML/AI. The Principal ML Engineer will define technical direction, evaluate legacy systems, lead proof-of-concept initiatives, and guide long-term platform decisions around LLMs, RAG, hybrid retrieval, and agentic AI systems. The role is not focused on day-to-day coding or people management; instead, it requires exceptional technical depth, architectural judgment, and the ability to translate complex technical concepts into clear recommendations for technical and non-technical stakeholders. Responsibilities:

• Serve as the principal technical authority for Machine Learning and GenAI initiatives.
• Define and communicate technical roadmaps, architectural direction, and long-term AI strategy.
• Evaluate legacy systems and platforms, identifying technical constraints, risks, dependencies, and modernization opportunities.
• Make high-impact technical decisions that guide engineering teams and influence platform evolution.
• Lead and design POCs to validate new ML, GenAI, tooling, frameworks, and architectural approaches.
• Assess feasibility, scalability, integration requirements, and operational considerations before broader adoption.
• Translate experimental results into clear architectural recommendations and implementation direction.
• Understand complex backend systems, data flows, APIs, microservices, integrations, and legacy codebases.
• Bridge historical system architecture with future-state ML and AI capabilities.
• Partner closely with engineering and architecture teams to ensure strategic decisions align with implementation realities.
• Guide the design and delivery of LLM, RAG, hybrid retrieval, and agentic workflow patterns.
• Define experimentation and evaluation strategies for AI systems.
• Establish quality metrics and monitoring approaches that identify degradation in AI system performance.
• Determine how AI results should be measured, improved, and continuously tuned.
• Guide decisions around dataset chunking, embedding strategies, pre-filtering, keyword augmentation, semantic search, hybrid retrieval, reranking, and intelligence thresholds.
• Help establish golden datasets and evaluation approaches that define target system performance.
• Evaluate agentic systems and determine appropriate quality metrics and supervision mechanisms.
• Maintain awareness of emerging agentic frameworks and determine where new approaches can provide architectural value.
• Provide technical leadership across engineering, product, platform, and architecture teams., *We have partnered with our client in their search for a Hands-on Sr Lead Data Scientist. Responsibilities: Working closely with other data scientists and engineers to design, d…

• 10+ years of overall technology experience.
• Early-career experience as a Software Engineer / Developer.
• 5+ years of experience in Machine Learning, Artificial Intelligence, or Generative AI.
• Strong foundation in Java and/or Python, including the ability to read, understand, and assess legacy code.
• Experience with or willingness to learn Go and other modern software languages.
• Advanced experience with ML, AI, or GenAI at the architectural and system-design level.
• Strong understanding of backend engineering, microservices, APIs, system design, and data-driven architectures.
• Strong cloud computing and distributed-systems knowledge.
• Experience with LLMs, RAG, hybrid retrieval, and agentic workflows.
• Strong understanding of experimentation, evaluation, benchmarking, and production monitoring for AI systems.
• Ability to make technical decisions at scale without direct ownership of day-to-day coding.
• Demonstrated technical leadership without direct people-management responsibilities.
• Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.

Preferred Qualifications:

• Strong Python and Java experience with working familiarity in Go, shell scripting, Git/GitHub, Docker, Kubernetes, and Helm.
• AWS experience, particularly EKS, cloud deployment patterns, and GitOps workflows.
• Experience with CI/CD and modern software delivery practices.
• Experience designing production-oriented RAG and agentic AI systems.
• Experience with vector/embedding strategies, hybrid search, reranking, semantic search, and retrieval evaluation.
• Experience developing golden datasets and evaluation frameworks for AI systems.
• Experience determining quality metrics for monitoring AI/agentic system degradation.
• Experience working with complex legacy modernization initiatives.